Anecdotes abound suggesting that the use of predictive analytics boosts firm performance. However, large-scale representative data on this phenomenon have been lacking. Working with the Census Bureau, we surveyed over 30,000 American manufacturing establishments on their use of predictive analytics and detailed workplace characteristics. We find that productivity is significantly higher among plants that use predictive analytics—up to $918,000 higher sales compared to similar competitors. Furthermore, both instrumental variables estimates and the timing of gains suggest a causal relationship. However, we find that the productivity pay-off only occurs when predictive analytics are combined with at least one of three workplace complements: significant accumulation of IT capital, educated workers, or workplaces designed for high flow-efficiency production. Our findings support claims that predictive analytics can substantially boost performance, while also explaining why some firms see no benefits at all.
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Prior work has addressed the measurement challenge associated with tracking analytics use in firms by triangulating on the human capital needed to adopt it, typically in smaller samples (Tambe 2014; Wu et al. 2019, 2020). In contrast, our data cover more than half of the U.S. manufacturing economy in the annual certainty sample. We view our approach to be complementary, with distinct advantages and challenges. See Section 2.
This is a common flexible approach to estimating revenue-based total-factor productivity (TFPR), and is possible due to establishment-level Census data on both expenditures and capital investment over time (e.g., Bloom et al. 2019).
This definition was corroborated in our study via extensive testing of the survey instrument, led by Census experts on survey development. While of significant importance in current research and business press, the actual use of machine learning and other cognitive technologies increasingly referred to as “artificial intelligence” was still quite low in the U.S. as of 2018, including in the manufacturing sector studied here (Zolas et al. 2020).
However, research on the value of big data shows that an increase in the amount of data available to firms has positive but diminishing impacts on prediction accuracy (Bajari et al. 2019).
Note that sample counts are rounded to comply with Census disclosure-avoidance requirements throughout the paper. We use the total number of observations (~51,000) as our baseline sample, but all key results are robust to restricting attention to a subsample for which respondent tenure dates back to at least one year before the recall reference year. This has been found to reduce measurement error for the other management practices measured in the MOPS (Bloom et al. 2019).
We also explore using a normalized score based on taking the average of multiple responses for a given establishment (see Bloom et al. 2019) and find results consistent to the top counted frequency measure.
The ASM is conducted annually, except for years ending in 2 and 7, when it is included in the CMF. This allows us to construct a panel for all ASM/CMF variables between 2010 to 2015, which we use in our timing test to rule out reverse causality.
Correlations between predictive analytics and plant size and age are available upon request.
The rotation of the ASM sample in years ending with 4 and 9 limits the number of establishments that have complete data for both reference years. However, a core “certainty sample” of larger plants covering the majority of economic activity in this sector is present for both years, conditional on survival.
The adoption of predictive analytics increases from 73 percent in 2010 to 80 percent by 2015.
High cross-sectional heterogeneity in firm performance has long been established (e.g., Syverson 2004 and 2011; Hopenhayn 2014); however, a large number of recent studies point to increasing firm heterogeneity along a number of economically important dimensions (Andrews et al. 2015; Van Reenen 2018; Song et al. 2019; Decker et al. 2020; Autor et al. 2020; Bennett 2020a). This phenomenon is not restricted to the United States (e.g., Berlingieri et al. 2017) and is a burgeoning area of research and public policy concern.
Results are quite insensitive to the choice of depreciation rate.
A reasonable concern here is that this measure fails to capture the effect of capitalized software (e.g., ERP investment), which might also play a significant role in facilitating the implementation of predictive analytics or otherwise boost productivity (Bessen and Righi 2019; Barth et al. 2020). To address this concern, we conduct several robustness tests in both the baseline performance analysis and the complementarity tests. First, we control separately for software and IT services expenditures from the ASM, in addition to IT capital stock. Alternatively, we use a measure summing up all IT investments (hardware, software, and services) instead of the IT capital stock variable. In both cases, our findings remain consistent.
See Kiran (2019) for a detailed description of cellular manufacturing.
This will happen if plants with higher expected returns to predictive analytics use will choose to adopt, upwardly biasing estimates of the average treatment effect. Tambe and Hitt (2012) provide a useful discussion of this common concern in the IT productivity literature, suggesting that such concerns may be overemphasized. System GMM and other semi-structural estimation methods (see Arellano and Bond 1991; Blundell and Bond 2000; Levinsohn and Petrin 2003; Ackerberg et al. 2015) have performed well in recent studies of IT productivity (e.g., Tambe and Hitt 2012; Nagle 2019), and point to quite limited upward bias due to self-selection. Unfortunately, our two-year panel lacks the longer lags typically required for this estimation approach.
Abundant anecdotes support the prevalence of this phenomenon. The Occupational Safety & Health Administration (OSHA) Recordkeeping rule can serve as another example: they require about 1.5 million employers in the United States to keep records of their employees’ work-related injuries and illnesses under the Occupational Safety and Health Act of 1970. For more details on OHSA Recordkeeping rule, see the OSHA website: https://www.osha.gov/recordkeeping2014/records.html.
Note that plants already collecting and using data extensively may be less responsive to our instrument, which we discuss below.
These controls are motivated by prior work associating them with technology adoption and productivity. Our management index differs from that in Bloom et al. (2019) by excluding the data-related MOPS questions. See Dunne (1994) and Foster et al. (2016) for more on the relationships between plant age, technology adoption, and performance. See Collis et al. (2007) for discussion of multi-unit and headquarter status. See Safizadeh et al. (1996) for more on manufacturing process designs. The indicator for multi-unit status equals one if the plants belong to multi-unit firms. We access the headquarter (HQ) status of a plant from the MOPS survey data where we define the HQ indicator equal to one if the plant is reported to be the HQ of a firm. Please see the definition of our measure for production process design in Table 2 (e.g., from the MOPS 2015). This set of controls is in all fully specified models for adoption and performance analysis unless stated otherwise.
For easy interpretation, we treat the frequency of predictive analytics as continuous variable (Long and Freese 2006). Results from additional tests treating it as ordinal are largely consistent and available upon request.
Our results are also robust to using labor productivity and estimated TFP (e.g., the conventional 4-factor TFP using cost of material, energy, labor, and capital stock following Bartelsman and Gray 1996; Foster et al. 2008) as alternative output measures. It is also robust to estimating a translog production function. Results are omitted due to space limitations but available upon request.
Using the index for frequency of predictive analytics for the IV estimations avoids potential complications due to non-linear first-stage estimation, and also better captures variation in plant use of predictive analytics.
Regression results for Figure 3 available upon request.
Based on the constant term in the linear probability model using the adoption of predictive analytics as our dependent variable, which represents the average adoption controlling for all covariates in our model. Results are omitted due to space limitations but available upon request.
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Disclaimer Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. All errors are our own. The authors thank the Stanford Digital Economy Lab and the MIT Initiative on the Digital Economy, the Social Science and Humanities Research Council of Canada (SSHRC), and the Kauffman Foundation for generous funding, as well as Chris Forman, Shane Greenstein, Megan MacGarvie, Ali Tafti, Lynn Wu and participants at the Annual Conference of the National Association of Business Economics, the Conference on Information Systems and Technology (CIST), the Workshop on Information Systems and Economics (WISE), the NBER Productivity Lunch Seminar, the MIT Initiative Lunch Seminar, and the Wharton Technology Conference for valuable comments.
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Brynjolfsson, E., Jin, W. & McElheran, K. The power of prediction: predictive analytics, workplace complements, and business performance. Bus Econ 56, 217–239 (2021). https://doi.org/10.1057/s11369-021-00224-5